FEATURE-African software developers are using AI to tackle inequality

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* AI is used to fight poverty and translate languages ​​* African data is considered essential to fight racial prejudice

* More funding, broader digital access needed, say developers By Kim Harrisberg

DURBAN, Feb 16 (Thomson Reuters Foundation) – Determined to use her skills to tackle inequality, South African computer scientist Raesetje Sefala has set to work creating algorithms that flag poverty hotspots – developing datasets that she hopes will help target aid, new housing or clinics. From crop analysis https://news.trust.org/item/20211014092853-b4qj7 to medical diagnostics, artificial intelligence (AI) is already being used in critical tasks around the world, but Sefala and a growing number other African developers are pioneering it to tackle the particular challenges of their continent.

Local knowledge is key to designing AI-based solutions that work, Sefala said. “If you don’t have people with diverse backgrounds to do the research, it’s easy to interpret the data in ways that marginalize others,” the 26-year-old said from her home in Johannesburg.

Africa is the world’s youngest and fastest growing continent, and tech experts say young, local AI developers have a vital role to play in designing apps to solve problems. local issues. “Bringing Africa out of poverty will take innovation and that can be game-changing because it is Africans who are doing things for Africa on their own,” said Cina Lawson, Togolese Minister of digital economy and transformation.

“We need to use cutting-edge solutions to our problems, because you’re not solving problems in 2022 using methods from 20 years ago,” Lawson told the Thomson Reuters Foundation in a video interview from that country. ‘West Africa. Digital rights groups warn against use of AI in surveillance https://news.trust.org/item/20211020225934-jceq3 and risk of discrimination https://news.trust.org /item/20210809090018-c8r11, but Sefala said it can also be used to “serve the people behind the data points”.

She mapped every suburb and township in South Africa, then combined that dataset with satellite data and machine learning algorithms to capture the growth of those neighborhoods over time. She soon realized that the algorithms she had built didn’t go that far because some townships – including the one where she grew up – weren’t predicted correctly.

Being able to fine-tune the algorithms based on her lived experience meant that the data collected became more accurate. “These kinds of decisions determine who you alienate or include when you build your AI models,” said Sefala, the senior AI researcher at the Distributed AI Research Institute (DAIR) — a community-based research group.

As COVID-19 spread around the world in early 2020, Togolese government officials realized urgent action was needed to support informal workers who make up around 80% of the country’s workforce, said Lawson said.

“If you decide that everyone is staying home, that means that particular person is not going to eat that day, it’s as simple as that,” she said. In 10 days, the government built a mobile payment platform – called Novissi – to distribute money to vulnerable people.

The government has partnered with the Innovations for Poverty Action (IPA) think tank and the University of California, Berkeley to map Togo’s poverty using satellite imagery. Using algorithms with support from GiveDirectly, a non-profit organization that uses AI to distribute cash transfers https://news.trust.org/item/20201211185008-xlm6l, recipients earn less than 1.25 $ per day and living in the poorest neighborhoods have been identified for direct cash transfer.

“We texted them saying if you need financial assistance, please register,” Lawson said, adding that recipient consent and data privacy had been prioritized. The entire program reached 920,000 beneficiaries in need.

“Machine learning has the advantage of reaching so many people in a very short time and providing help when people need it most,” said Caroline Teti, director of GiveDirectly. based in Kenya. ‘ZERO REPRESENTATION’

Aiming to spur discussions on AI in Africa, computer scientists Benjamin Rosman and Ulrich Paquet co-founded the Deep Learning Indaba – a week-long gathering that began in South Africa – with other colleagues in 2017. “You used to get to the top AI conferences and there was no representation from Africa, both in terms of papers and people, so we’re all looking to find cost-effective ways to create a community,” Paquet said in a video call.

In 2019, 27 smaller Indabas – called IndabaX – were rolled out across the continent, with some events hosting up to 300 attendees. One such offshoot was IndabaX Uganda, where founder Bruno Ssekiwere said attendees shared information about using AI for social issues such as improving agriculture and treating malaria.

Another output from the South African Indaba was Masakhane – an organization that uses open source machine learning to translate African languages ​​not typically found in online programs such as Google Translate. On their site, the founders talk about the South African philosophy of “Ubuntu” – a term generally meaning “humanity” – as part of their organization’s values.

“This philosophy calls for collaboration, participation and community,” their site reads, a philosophy which, according to Ssekiwere, Paquet and Rosman, has now become the driving force behind AI research in Africa. . INCLUSION

Now that Sefala has built a dataset on South Africa’s suburbs and townships, she plans to collaborate with subject matter experts and communities to refine it, further research on inequalities, and improve algorithms. “Making datasets easily accessible opens the door to new mechanisms and techniques for policy-making regarding desegregation, housing and access to economic opportunity,” she said.

African AI leaders say creating more comprehensive datasets will also help tackle biases embedded in algorithms. “Imagine the deployment of Novissi in Benin, Burkina Faso, Ghana, Ivory Coast…then the algorithm will be trained to understand poverty in West Africa,” Lawson said.

“If there’s a way to fight bias in technology, it’s by increasing the diversity of datasets…we need to contribute more,” she said. But contributing more will require increased funding for African projects and wider access to computer education and technology in general, Sefala said.

Despite these hurdles, Lawson said “technology will be Africa’s saviour.” “Let’s use what is cutting edge and apply it right away or as a continent we will never get out of poverty,” she said. “It really is that simple.”

(This story has not been edited by the Devdiscourse team and is auto-generated from a syndicated feed.)

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